TECHNICAL FIELD
[0001] The subject matter of this disclosure relates in general to the field of networking
security, and more specifically to systems and methods of generating an application
protectability index for network applications and a corresponding protectability scheme.
BACKGROUND
[0002] With the expanding complexity of networking environments, an application running
on a server available on an enterprise network may be protected at various network
layers, such as firewalls, containers, load balancers, etc. Currently, network monitoring
systems used to monitor and manage operations and traffic within an enterprise network
lack a process for providing an objective assessment of protectability of any given
application at various network layers that can be used to improve the protectability
thereof within the enterprise network.
US2012/210434 discloses a security countermeasure management platform.
BRIEF DESCRIPTION OF THE FIGURES
[0003] In order to describe the manner in which the above-recited and other advantages and
features of the disclosure can be obtained, a more particular description of the principles
briefly described above will be rendered by reference to specific embodiments that
are illustrated in the appended drawings. Understanding that these drawings depict
only embodiments of the disclosure and are not therefore to be considered to be limiting
of its scope, the principles herein are described and explained with additional specificity
and detail through the use of the accompanying drawings in which:
FIG. 1 illustrates an example of a network traffic monitoring system, according to
one aspect of the present disclosure;
FIG. 2 illustrates an example of a network environment, according to one aspect of
the present disclosure;
FIG. 3 illustrates an example of a data pipeline for generating network insights based
on collected network information, according to one aspect of the present disclosure;
FIGs. 4A and 4B illustrate examples of an application security assessment system,
according to one aspect of the present disclosure;
FIG. 5 illustrates an example method for determining an application protectability
index, according to one aspect of the present disclosure; and
FIG. 6 illustrates an example computing system, according to one aspect of the present
disclosure.
DESCRIPTION OF EXAMPLE EMBODIMENTS
[0004] Various embodiments of the disclosure are discussed in detail below. While specific
implementations are discussed, it should be understood that this is done for illustration
purposes only. A person skilled in the relevant art will recognize that other components
and configurations may be used without parting from the spirit and scope of the disclosure.
Thus, the following description and drawings are illustrative and are not to be construed
as limiting. Numerous specific details are described to provide a thorough understanding
of the disclosure. However, in certain instances, well-known or conventional details
are not described in order to avoid obscuring the description. References to one or
an embodiment in the present disclosure can be references to the same embodiment or
any embodiment; and, such references mean at least one of the embodiments.
[0005] Reference to "one embodiment" or "an embodiment" means that a particular feature,
structure, or characteristic described in connection with the embodiment is included
in at least one embodiment of the disclosure. The appearances of the phrase "in one
embodiment" in various places in the specification are not necessarily all referring
to the same embodiment, nor are separate or alternative embodiments mutually exclusive
of other embodiments. Moreover, various features are described which may be exhibited
by some embodiments and not by others.
[0006] The terms used in this specification generally have their ordinary meanings in the
art, within the context of the disclosure, and in the specific context where each
term is used. Alternative language and synonyms may be used for any one or more of
the terms discussed herein, and no special significance should be placed upon whether
or not a term is elaborated or discussed herein. In some cases, synonyms for certain
terms are provided. A recital of one or more synonyms does not exclude the use of
other synonyms. The use of examples anywhere in this specification including examples
of any terms discussed herein is illustrative only, and is not intended to further
limit the scope and meaning of the disclosure or of any example term. Likewise, the
disclosure is not limited to various embodiments given in this specification.
[0007] Without intent to limit the scope of the disclosure, examples of instruments, apparatus,
methods and their related results according to the embodiments of the present disclosure
are given below. Note that titles or subtitles may be used in the examples for convenience
of a reader, which in no way should limit the scope of the disclosure. Unless otherwise
defined, technical and scientific terms used herein have the meaning as commonly understood
by one of ordinary skill in the art to which this disclosure pertains. In the case
of conflict, the present document, including definitions will control.
[0008] Additional features and advantages of the disclosure will be set forth in the description
which follows, and in part will be obvious from the description, or can be learned
by practice of the herein disclosed principles. The features and advantages of the
disclosure can be realized and obtained by means of the instruments and combinations
particularly pointed out in the appended claims. These and other features of the disclosure
will become more fully apparent from the following description and appended claims,
or can be learned by the practice of the principles set forth herein.
Overview
[0009] Aspects of the present disclosure are set out in the independent claims and preferred
features are set out in the dependent claims. Features of one aspect may be applied
to any aspect alone or in combination with other aspects.
[0010] Disclosed herein are methods, systems, and non-transitory computer-readable storage
media for generating an application protectability index for network applications
and a corresponding protectability scheme. More specifically, an objective assessment
of protectability of an application at any one of multiple network layers is generated,
which are then used to generate an application protectability index. As will be described
below, the application protectability index is used to create a protectability scheme
(or replace/modify existing protectability scheme) for the corresponding application.
[0011] In one aspect, a method includes identifying, by a network controller, network layers
associated with an application; determining, by the network controller, a corresponding
security index for the application at each of the network layers to yield a plurality
of security indexes, each of the plurality of security indexes providing an objective
assessment of protectability of the application at a corresponding one of the network
layers; determining, by the network controller, an application protectability index
based on the plurality of security indexes; and generating an application protectability
scheme for protecting the application based on the application protectability index.
[0012] In another aspect, the method for determining the corresponding security index at
each of the network layers includes identifying a number of tools at a corresponding
network layer available for protecting the application; assigning a corresponding
weight to each of the tools; determining a corresponding protectability index factor
for each of the tools; assigning a weight to the corresponding network layer; and
determining the corresponding security index based on the corresponding weight of
each of the tools, the corresponding protectability index factor for each of the tools
and the weight of the corresponding network layer.
[0013] In another aspect, determining the corresponding security index at each of the network
layers is based on a sum of all protectability indexes for the tools weighted by the
corresponding weight assigned to each of the tools.
[0014] In another aspect, the corresponding weight of each of the tools is an objective
indication of a level of protection provided by a corresponding one of the tools.
[0015] In another aspect, the weight of the corresponding network layer is indicative of
importance of the corresponding network layer in protecting the application relative
to remaining ones of the network layers.
[0016] In another aspect, the application protectability index is determined as a ratio
of a sum of the plurality of security indexes to a sum of all weights assigned to
the network layers.
[0017] In another aspect, the application protectability index includes at least one recommendation
for improving protectability of the application.
[0018] In one aspect, a system includes at least one processor and a non-transitory computer-readable
storage medium including instructions stored thereon which, when executed by the at
least one processors, cause the at least one processors to identify, by a network
controller, network layers associated with an application; determine, by the network
controller, a corresponding security index for the application at each of the network
layers to yield a plurality of security indexes, each of the plurality of security
indexes providing an objective assessment of protectability of the application at
a corresponding one of the network layers; determine, by the network controller, an
application protectability index based on the plurality of security indexes; and generate
an application protectability scheme for protecting the application based on the application
protectability index.
[0019] In one aspect, a non-transitory computer-readable storage medium has stored thereon
instructions which, when executed by a processor, cause the processor to identify,
by a network controller, network layers associated with an application; determine,
by the network controller, a corresponding security index for the application at each
of the network layers to yield a plurality of security indexes, each of the plurality
of security indexes providing an objective assessment of protectability of the application
at a corresponding one of the network layers; determine, by the network controller,
an application protectability index based on the plurality of security indexes; and
generate an application protectability scheme for protecting the application based
on the application protectability index.
Description
[0020] Various embodiments of the disclosure are discussed in detail below. While specific
implementations are discussed, it should be understood that this is done for illustrative
purposes only. A person skilled in the relevant art will recognize that other components
and configurations may be used without departing from the spirit and scope of the
disclosure.
[0021] The disclosed technology addresses the need in the art for assessing application
protectability and generating a scheme for application protectability. The present
technology involves methods, systems, and non-transitory computer-readable media for
identifying security layers, assessing the protectability of these security layers
for an application, and generating protectability schemes to improve application security.
More specifically, an objective assessment of protectability of an application at
any one of multiple network layers is provided, which are then used to generate an
application protectability Index. As will be described below, the application protectability
index is used to create a protectability scheme (or replace/modify existing protectability
scheme) for the corresponding application.
[0022] The present technologies will be described in more detail in the disclosure as follows.
The disclosure begins with an initial discussion of systems and technologies for monitoring
network activity. A description of example systems, methods, and environments for
this monitoring technology will be discussed in FIGs. 1 through 3. The disclosure
will then continue with a discussion of methods, systems, and non-transitory computer-readable
media for identifying security layers, assessing the protectability of these security
layers for an application, and generating protectability schemes to improve application
security, as shown in FIGs. 4 and 5. The disclosure concludes with a description of
an example computing system, described in FIG. 6, which may comprise an element of
the systems shown in FIGs. 1 through 4.
[0023] The disclosure now turns to an initial discussion of example systems and technologies
for monitoring network activity.
[0024] Sensors deployed in a network can be used to gather network information related to
network traffic of nodes operating in the network and process information for nodes
and applications running in the network. Gathered network information can be analyzed
to provide insights into the operation of the nodes in the network, otherwise referred
to as analytics. In particular, discovered application or inventories, application
dependencies, policies, efficiencies, resource and bandwidth usage, and network flows
can be determined for the network using the network traffic data. For example, an
analytics engine can be configured to automate discovery of applications running in
the network, map the applications' interdependencies, or generate a set of proposed
network policies for implementation.
[0025] The analytics engine can monitor network information, process information, and other
relevant information of traffic passing through the network using a sensor network
that provides multiple perspectives for the traffic. The sensor network can include
sensors for networking devices (e.g., routers, switches, network appliances), physical
servers, hypervisors or shared kernels, and virtual partitions (e.g., VMs or containers),
and other network elements. The analytics engine can analyze the network information,
process information, and other pertinent information to determine various network
insights.
[0026] Referring now to the drawings, FIG. 1 illustrates an example of a network traffic
monitoring system, according to one aspect of the present disclosure. The network
traffic monitoring system 100 can include a configuration manager 102, sensors 104,
a collector module 106, a data mover module 108, an analytics engine 110, and a presentation
module 112. In FIG. 1, the analytics engine 110 is also shown in communication with
out-of-band data sources 114, third party data sources 116, and a network controller
118.
[0027] The configuration manager 102 can be used to provision and maintain the sensors 104,
including installing sensor software or firmware in various nodes of a network, configuring
the sensors 104, updating the sensor software or firmware, among other sensor management
tasks. For example, the sensors 104 can be implemented as virtual partition images
(e.g., virtual machine (VM) images or container images), and the configuration manager
102 can distribute the images to host machines. In general, a virtual partition may
be an instance of a VM, container, sandbox, or other isolated software environment.
The software environment may include an operating system and application software.
For software running within a virtual partition, the virtual partition may appear
to be, for example, one of many servers or one of many operating systems executed
on a single physical server. The configuration manager 102 can instantiate a new virtual
partition or migrate an existing partition to a different physical server. The configuration
manager 102 can also be used to configure the new or migrated sensor.
[0028] The configuration manager 102 can monitor the health of the sensors 104. For example,
the configuration manager 102 may request status updates and/or receive heartbeat
messages, initiate performance tests, generate health checks, and perform other health
monitoring tasks. In some embodiments, the configuration manager 102 can also authenticate
the sensors 104. For instance, the sensors 104 can be assigned a unique identifier,
such as by using a one-way hash function of a sensor's basic input/out system (BIOS)
universally unique identifier (UUID) and a secret key stored by the configuration
image manager 102. The UUID can be a large number that may be difficult for a malicious
sensor or other device or component to guess. In some embodiments, the configuration
manager 102 can keep the sensors 104 up to date by installing the latest versions
of sensor software and/or applying patches. The configuration manager 102 can obtain
these updates automatically from a local source or the Internet.
[0029] The sensors 104 can reside on various nodes of a network, such as a virtual partition
(e.g., VM or container) 120; a hypervisor or shared kernel managing one or more virtual
partitions and/or physical servers 122, an application-specific integrated circuit
(ASIC) 124 of a switch, router, gateway, or other networking device, or a packet capture
(pcap) 126 appliance (e.g., a standalone packet monitor, a device connected to a network
devices monitoring port, a device connected in series along a main trunk of a datacenter,
or similar device), or other element of a network. The sensors 104 can monitor network
traffic between nodes, and send network traffic data and corresponding data (e.g.,
host data, process data, user data, etc.) to the collectors 106 for storage. For example,
the sensors 104 can sniff packets being sent over its hosts' physical or virtual network
interface card (NIC), or individual processes can be configured to report network
traffic and corresponding data to the sensors 104. Incorporating the sensors 104 on
multiple nodes and within multiple partitions of some nodes of the network can provide
for robust capture of network traffic and corresponding data from each hop of data
transmission. In some embodiments, each node of the network (e.g., VM, container,
or other virtual partition 120, hypervisor, shared kernel, or physical server 122,
ASIC 124, pcap 126, etc.) includes a respective sensor 104. However, it should be
understood that various software and hardware configurations can be used to implement
the sensor network 104.
[0030] As the sensors 104 capture communications and corresponding data, they may continuously
send network traffic data to the collectors 106. The network traffic data can include
metadata relating to a packet, a collection of packets, a flow, a bidirectional flow,
a group of flows, a session, or a network communication of another granularity. That
is, the network traffic data can generally include any information describing communication
on all layers of the Open Systems Interconnection (OSI) model. For example, the network
traffic data can include source/destination MAC address, source/destination IP address,
protocol, port number, etc. In some embodiments, the network traffic data can also
include summaries of network activity or other network statistics such as number of
packets, number of bytes, number of flows, bandwidth usage, response time, latency,
packet loss, jitter, and other network statistics.
[0031] The sensors 104 can also determine additional data for each session, bidirectional
flow, flow, packet, or other more granular or less granular network communication.
The additional data can include host and/or endpoint information, virtual partition
information, sensor information, process information, user information, tenant information,
application information, network topology, application dependency mapping, cluster
information, or other information corresponding to each flow.
[0032] In some embodiments, the sensors 104 can perform some preprocessing of the network
traffic and corresponding data before sending the data to the collectors 106. For
example, the sensors 104 can remove extraneous or duplicative data or they can create
summaries of the data (e.g., latency, number of packets per flow, number of bytes
per flow, number of flows, etc.). In some embodiments, the sensors 104 can be configured
to only capture certain types of network information and disregard the rest. In some
embodiments, the sensors 104 can be configured to capture only a representative sample
of packets (e.g., every 1,000th packet or other suitable sample rate) and corresponding
data.
[0033] Since the sensors 104 may be located throughout the network, network traffic and
corresponding data can be collected from multiple vantage points or multiple perspectives
in the network to provide a more comprehensive view of network behavior. The capture
of network traffic and corresponding data from multiple perspectives rather than just
at a single sensor located in the data path or in communication with a component in
the data path, allows the data to be correlated from the various data sources, which
may be used as additional data points by the analytics engine 110. Further, collecting
network traffic and corresponding data from multiple points of view ensures more accurate
data is captured. For example, other types of sensor networks may be limited to sensors
running on external-facing network devices (e.g., routers, switches, network appliances,
etc.) such that east-west traffic, including VM-to-VM or container-to-container traffic
on a same host, may not be monitored. In addition, packets that are dropped before
traversing a network device or packets containing errors may not be accurately monitored
by other types of sensor networks. The sensor network 104 of various embodiments substantially
mitigates or eliminates these issues altogether by locating sensors at multiple points
of potential failure. Moreover, the network traffic monitoring system 100 can verify
multiple instances of data for a flow (e.g., source endpoint flow data, network device
flow data, and endpoint flow data) against one another.
[0034] In some embodiments, the network traffic monitoring system 100 can assess a degree
of accuracy of flow data sets from multiple sensors and utilize a flow data set from
a single sensor determined to be the most accurate and/or complete. The degree of
accuracy can be based on factors such as network topology (e.g., a sensor closer to
the source may be more likely to be more accurate than a sensor closer to the destination),
a state of a sensor or a node hosting the sensor (e.g., a compromised sensor/node
may have less accurate flow data than an uncompromised sensor/node), or flow data
volume (e.g., a sensor capturing a greater number of packets for a flow may be more
accurate than a sensor capturing a smaller number of packets).
[0035] In some embodiments, the network traffic monitoring system 100 can assemble the most
accurate flow data set and corresponding data from multiple sensors. For instance,
a first sensor along a data path may capture data for a first packet of a flow but
may be missing data for a second packet of the flow while the situation is reversed
for a second sensor along the data path. The network traffic monitoring system 100
can assemble data for the flow from the first packet captured by the first sensor
and the second packet captured by the second sensor.
[0036] As discussed, the sensors 104 can send network traffic and corresponding data to
the collectors 106. In some embodiments, each sensor can be assigned to a primary
collector and a secondary collector as part of a high availability scheme. If the
primary collector fails or communications between the sensor and the primary collector
are not otherwise possible, a sensor can send its network traffic and corresponding
data to the secondary collector. In other embodiments, the sensors 104 are not assigned
specific collectors but the network traffic monitoring system 100 can determine an
optimal collector for receiving the network traffic and corresponding data through
a discovery process. In such embodiments, a sensor can change where it sends it network
traffic and corresponding data if its environments changes, such as if a default collector
fails or if the sensor is migrated to a new location and it would be optimal for the
sensor to send its data to a different collector. For example, it may be preferable
for the sensor to send its network traffic and corresponding data on a particular
path and/or to a particular collector based on latency, shortest path, monetary cost
(e.g., using private resources versus a public resources provided by a public cloud
provider), error rate, or some combination of these factors. In other embodiments,
a sensor can send different types of network traffic and corresponding data to different
collectors. For example, the sensor can send first network traffic and corresponding
data related to one type of process to one collector and second network traffic and
corresponding data related to another type of process to another collector.
[0037] The collectors 106 can be any type of storage medium that can serve as a repository
for the network traffic and corresponding data captured by the sensors 104. In some
embodiments, data storage for the collectors 106 is located in an in-memory database,
such as dashDB from IBM
®, although it should be appreciated that the data storage for the collectors 106 can
be any software and/or hardware capable of providing rapid random access speeds typically
used for analytics software. In various embodiments, the collectors 106 can utilize
solid state drives, disk drives, magnetic tape drives, or a combination of the foregoing
according to cost, responsiveness, and size requirements. Further, the collectors
106 can utilize various database structures such as a normalized relational database
or a NoSQL database, among others.
[0038] In some embodiments, the collectors 106 may only serve as network storage for the
network traffic monitoring system 100. In such embodiments, the network traffic monitoring
system 100 can include a data mover module 108 for retrieving data from the collectors
106 and making the data available to network clients, such as the components of the
analytics engine 110. In effect, the data mover module 108 can serve as a gateway
for presenting network-attached storage to the network clients. In other embodiments,
the collectors 106 can perform additional functions, such as organizing, summarizing,
and preprocessing data. For example, the collectors 106 can tabulate how often packets
of certain sizes or types are transmitted from different nodes of the network. The
collectors 106 can also characterize the traffic flows going to and from various nodes.
In some embodiments, the collectors 106 can match packets based on sequence numbers,
thus identifying traffic flows and connection links. As it may be inefficient to retain
all data indefinitely in certain circumstances, in some embodiments, the collectors
106 can periodically replace detailed network traffic data with consolidated summaries.
In this manner, the collectors 106 can retain a complete dataset describing one period
(e.g., the past minute or other suitable period of time), with a smaller dataset of
another period (e.g., the previous 2-10 minutes or other suitable period of time),
and progressively consolidate network traffic and corresponding data of other periods
of time (e.g., day, week, month, year, etc.). In some embodiments, network traffic
and corresponding data for a set of flows identified as normal or routine can be winnowed
at an earlier period of time while a more complete data set may be retained for a
lengthier period of time for another set of flows identified as anomalous or as an
attack.
[0039] Computer networks may be exposed to a variety of different attacks that expose vulnerabilities
of computer systems in order to compromise their security. Some network traffic may
be associated with malicious programs or devices. The analytics engine 110 may be
provided with examples of network states corresponding to an attack and network states
corresponding to normal operation. The analytics engine 110 can then analyze network
traffic and corresponding data to recognize when the network is under attack. In some
embodiments, the network may operate within a trusted environment for a period of
time so that the analytics engine 110 can establish a baseline of normal operation.
Since malware is constantly evolving and changing, machine learning may be used to
dynamically update models for identifying malicious traffic patterns.
[0040] In some embodiments, the analytics engine 110 may be used to identify observations
which differ from other examples in a dataset. For example, if a training set of example
data with known outlier labels exists, supervised anomaly detection techniques may
be used. Supervised anomaly detection techniques utilize data sets that have been
labeled as normal and abnormal and train a classifier. In a case in which it is unknown
whether examples in the training data are outliers, unsupervised anomaly techniques
may be used. Unsupervised anomaly detection techniques may be used to detect anomalies
in an unlabeled test data set under the assumption that the majority of instances
in the data set are normal by looking for instances that seem to fit to the remainder
of the data set.
[0041] The analytics engine 110 can include a data lake 130, an application dependency mapping
(ADM) module 140, and elastic processing engines 150. The data lake 130 is a large-scale
storage repository that provides massive storage for various types of data, enormous
processing power, and the ability to handle nearly limitless concurrent tasks or jobs.
In some embodiments, the data lake 130 is implemented using the Hadoop
® Distributed File System (HDFS
™) from Apache
® Software Foundation of Forest Hill, Maryland. HDFS
™ is a highly scalable and distributed file system that can scale to thousands of cluster
nodes, millions of files, and petabytes of data. HDFS
™ is optimized for batch processing where data locations are exposed to allow computations
to take place where the data resides. HDFS
™ provides a single namespace for an entire cluster to allow for data coherency in
a write-once, read-many access model. That is, clients can only append to existing
files in the node. In HDFS
™, files are separated into blocks, which are typically 64 MB in size and are replicated
in multiple data nodes. Clients access data directly from data nodes.
[0042] In some embodiments, the data mover 108 receives raw network traffic and corresponding
data from the collectors 106 and distributes or pushes the data to the data lake 130.
The data lake 130 can also receive and store out-of-band data 114, such as statuses
on power levels, network availability, server performance, temperature conditions,
cage door positions, and other data from internal sources, and third party data 116,
such as security reports (e.g., provided by Cisco
® Systems, Inc. of San Jose, California, Arbor Networks
® of Burlington, Massachusetts, Symantec
® Corp. of Sunnyvale, California, Sophos
® Group plc of Abingdon, England, Microsoft
® Corp. of Seattle, Washington, Verizon
® Communications, Inc. of New York, New York, among others), geolocation data, IP watch
lists, Whois data, configuration management database (CMDB) or configuration management
system (CMS) as a service, and other data from external sources. In other embodiments,
the data lake 130 may instead fetch or pull raw traffic and corresponding data from
the collectors 106 and relevant data from the out-of-band data sources 114 and the
third party data sources 116. In yet other embodiments, the functionality of the collectors
106, the data mover 108, the out-of-band data sources 114, the third party data sources
116, and the data lake 130 can be combined. Various combinations and configurations
are possible as would be known to one of ordinary skill in the art.
[0043] Each component of the data lake 130 can perform certain processing of the raw network
traffic data and/or other data (e.g., host data, process data, user data, out-of-band
data or third party data) to transform the raw data to a form useable by the elastic
processing engines 150. In some embodiments, the data lake 130 can include repositories
for flow attributes 132, host and/or endpoint attributes 134, process attributes 136,
and policy attributes 138. In some embodiments, the data lake 130 can also include
repositories for VM or container attributes, application attributes, tenant attributes,
network topology, application dependency maps, cluster attributes, etc.
[0044] The flow attributes 132 relate to information about flows traversing the network.
A flow is generally one or more packets sharing certain attributes that are sent within
a network within a specified period of time. The flow attributes 132 can include packet
header fields such as a source address (e.g., Internet Protocol (IP) address, Media
Access Control (MAC) address, Domain Name System (DNS) name, or other network address),
source port, destination address, destination port, protocol type, class of service,
among other fields. The source address may correspond to a first endpoint (e.g., network
device, physical server, virtual partition, etc.) of the network, and the destination
address may correspond to a second endpoint, a multicast group, or a broadcast domain.
The flow attributes 132 can also include aggregate packet data such as flow start
time, flow end time, number of packets for a flow, number of bytes for a flow, the
union of TCP flags for a flow, among other flow data.
[0045] The host and/or endpoint attributes 134 describe host and/or endpoint data for each
flow, and can include host and/or endpoint name, network address, operating system,
CPU usage, network usage, disk space, ports, logged users, scheduled jobs, open files,
and information regarding files and/or directories stored on a host and/or endpoint
(e.g., presence, absence, or modifications of log files, configuration files, device
special files, or protected electronic information). As discussed, in some embodiments,
the host and/or endpoints attributes 134 can also include the out-of-band data 114
regarding hosts such as power level, temperature, and physical location (e.g., room,
row, rack, cage door position, etc.) or the third party data 116 such as whether a
host and/or endpoint is on an IP watch list or otherwise associated with a security
threat, Whois data, or geocoordinates. In some embodiments, the out-of-band data 114
and the third party data 116 may be associated by process, user, flow, or other more
granular or less granular network element or network communication.
[0046] The process attributes 136 relate to process data corresponding to each flow, and
can include process name (e.g., bash, httpd, netstat, etc.), ID, parent process ID,
path (e.g., /usr2/username/bin/, /usr/local/bin, /usr/bin, etc.), CPU utilization,
memory utilization, memory address, scheduling information, nice value, flags, priority,
status, start time, terminal type, CPU time taken by the process, the command that
started the process, and information regarding a process owner (e.g., user name, ID,
user's real name, e-mail address, user's groups, terminal information, login time,
expiration date of login, idle time, and information regarding files and/or directories
of the user).
[0047] The policy attributes 138 contain information relating to network policies. Policies
establish whether a particular flow is allowed or denied by the network as well as
a specific route by which a packet traverses the network. Policies can also be used
to mark packets so that certain kinds of traffic receive differentiated service when
used in combination with queuing techniques such as those based on priority, fairness,
weighted fairness, token bucket, random early detection, round robin, among others.
The policy attributes 138 can include policy statistics such as a number of times
a policy was enforced or a number of times a policy was not enforced. The policy attributes
138 can also include associations with network traffic data. For example, flows found
to be non-conformant can be linked or tagged with corresponding policies to assist
in the investigation of non-conformance.
[0048] The analytics engine 110 may include any number of engines 150, including for example,
a flow engine 152 for identifying flows (e.g., flow engine 152) or an attacks engine
154 for identify attacks to the network. In some embodiments, the analytics engine
can include a separate distributed denial of service (DDoS) attack engine 155 for
specifically detecting DDoS attacks. In other embodiments, a DDoS attack engine may
be a component or a sub-engine of a general attacks engine. In some embodiments, the
attacks engine 154 and/or the DDoS engine 155 can use machine learning techniques
to identify security threats to a network. For example, the attacks engine 154 and/or
the DDoS engine 155 can be provided with examples of network states corresponding
to an attack and network states corresponding to normal operation. The attacks engine
154 and/or the DDoS engine 155 can then analyze network traffic data to recognize
when the network is under attack. In some embodiments, the network can operate within
a trusted environment for a time to establish a baseline for normal network operation
for the attacks engine 154 and/or the DDoS.
[0049] The analytics engine 110 may further include a search engine 156. The search engine
156 may be configured, for example to perform a structured search, an NLP (Natural
Language Processing) search, or a visual search. Data may be provided to the engines
from one or more processing components.
[0050] The analytics engine 110 can also include a policy engine 158 that manages network
policy, including creating and/or importing policies, monitoring policy conformance
and non-conformance, enforcing policy, simulating changes to policy or network elements
affecting policy, among other policy-related tasks.
[0051] The ADM module 140 can determine dependencies of applications of the network. That
is, particular patterns of traffic may correspond to an application, and the interconnectivity
or dependencies of the application can be mapped to generate a graph for the application
(i.e., an application dependency mapping). In this context, an application refers
to a set of networking components that provides connectivity for a given set of workloads.
For example, in a three-tier architecture for a web application, first endpoints of
the web tier, second endpoints of the application tier, and third endpoints of the
data tier make up the web application. The ADM module 140 can receive input data from
various repositories of the data lake 130 (e.g., the flow attributes 132, the host
and/or endpoint attributes 134, the process attributes 136, etc.). The ADM module
140 may analyze the input data to determine that there is first traffic flowing between
external endpoints on port 80 of the first endpoints corresponding to Hypertext Transfer
Protocol (HTTP) requests and responses. The input data may also indicate second traffic
between first ports of the first endpoints and second ports of the second endpoints
corresponding to application server requests and responses and third traffic flowing
between third ports of the second endpoints and fourth ports of the third endpoints
corresponding to database requests and responses. The ADM module 140 may define an
ADM for the web application as a three-tier application including a first EPG comprising
the first endpoints, a second EPG comprising the second endpoints, and a third EPG
comprising the third endpoints.
[0052] The presentation module 112 can include an application programming interface (API)
or command line interface (CLI) 160, a security information and event management (SIEM)
interface 162, and a web front-end 164. As the analytics engine 110 processes network
traffic and corresponding data and generates analytics data, the analytics data may
not be in a human-readable form or it may be too voluminous for a user to navigate.
The presentation module 112 can take the analytics data generated by analytics engine
110 and further summarize, filter, and organize the analytics data as well as create
intuitive presentations for the analytics data.
[0053] In some embodiments, the API or CLI 160 can be implemented using Hadoop
® Hive from Apache
® for the back end, and Java
® Database Connectivity (JDBC) from Oracle
® Corporation of Redwood Shores, California, as an API layer. Hive is a data warehouse
infrastructure that provides data summarization and ad hoc querying. Hive provides
a mechanism to query data using a variation of structured query language (SQL) that
is called HiveQL. JDBC is an application programming interface (API) for the programming
language Java
®, which defines how a client may access a database.
[0054] In some embodiments, the SIEM interface 162 can be implemented using Kafka for the
back end, and software provided by Splunk
®, Inc. of San Francisco, California as the SIEM platform. Kafka is a distributed messaging
system that is partitioned and replicated. Kafka uses the concept of topics. Topics
are feeds of messages in specific categories. In some embodiments, Kafka can take
raw packet captures and telemetry information from the data mover 108 as input, and
output messages to a SIEM platform, such as Splunk
®. The Splunk
® platform is utilized for searching, monitoring, and analyzing machine-generated data.
[0055] In some embodiments, the web front-end 164 can be implemented using software provided
by MongoDB
®, Inc. of New York, New York and Hadoop
® ElasticSearch from Apache
® for the back-end, and Ruby on Rails
™ as the web application framework. MongoDB
® is a document-oriented NoSQL database based on documents in the form of JavaScript
® Object Notation (JSON) with dynamic schemas. ElasticSearch is a scalable and real-time
search and analytics engine that provides domain-specific language (DSL) full querying
based on JSON. Ruby on Rails
™ is model-view-controller (MVC) framework that provides default structures for a database,
a web service, and web pages. Ruby on Rails
™ relies on web standards such as JSON or extensible markup language (XML) for data
transfer, and hypertext markup language (HTML), cascading style sheets, (CSS), and
JavaScript
® for display and user interfacing.
[0056] Although FIG. 1 illustrates an example configuration of the various components of
a network traffic monitoring system, those of skill in the art will understand that
the components of the network traffic monitoring system 100 or any system described
herein can be configured in a number of different ways and can include any other type
and number of components. For example, the sensors 104, the collectors 106, the data
mover 108, and the data lake 130 can belong to one hardware and/or software module
or multiple separate modules. Other modules can also be combined into fewer components
and/or further divided into more components.
[0057] FIG. 2 illustrates an example of a network environment, according to one aspect of
the present disclosure. In some embodiments, a network traffic monitoring system,
such as the network traffic monitoring system 100 of FIG. 1, can be implemented in
the network environment 200. It should be understood that, for the network environment
200 and any environment discussed herein, there can be additional or fewer nodes,
devices, links, networks, or components in similar or alternative configurations.
Embodiments with different numbers and/or types of clients, networks, nodes, cloud
components, servers, software components, devices, virtual or physical resources,
configurations, topologies, services, appliances, deployments, or network devices
are also contemplated herein. Further, the network environment 200 can include any
number or type of resources, which can be accessed and utilized by clients or tenants.
The illustrations and examples provided herein are for clarity and simplicity.
[0058] The network environment 200 can include a network fabric 202, a Layer 2 (L2) network
204, a Layer 3 (L3) network 206, and servers 208a, 208b, 208c, 208d, and 208e (collectively,
208). The network fabric 202 can include spine switches 210a, 210b, 210c, and 210d
(collectively, "210") and leaf switches 212a, 212b, 212c, 212d, and 212e (collectively,
"212"). The spine switches 210 can connect to the leaf switches 212 in the network
fabric 202. The leaf switches 212 can include access ports (or non-fabric ports) and
fabric ports. The fabric ports can provide uplinks to the spine switches 210, while
the access ports can provide connectivity to endpoints (e.g., the servers 208), internal
networks (e.g., the L2 network 204), or external networks (e.g., the L3 network 206).
[0059] The leaf switches 212 can reside at the edge of the network fabric 202, and can thus
represent the physical network edge. For instance, in some embodiments, the leaf switches
212d and 212e operate as border leaf switches in communication with edge devices 214
located in the external network 206. The border leaf switches 212d and 212e may be
used to connect any type of external network device, service (e.g., firewall, deep
packet inspector, traffic monitor, load balancer, etc.), or network (e.g., the L3
network 206) to the fabric 202.
[0060] Although the network fabric 202 is illustrated and described herein as an example
leaf-spine architecture, one of ordinary skill in the art will readily recognize that
various embodiments can be implemented based on any network topology, including any
data center or cloud network fabric. Indeed, other architectures, designs, infrastructures,
and variations are contemplated herein. For example, the principles disclosed herein
are applicable to topologies including three-tier (including core, aggregation, and
access levels), fat tree, mesh, bus, hub and spoke, etc. Thus, in some embodiments,
the leaf switches 212 can be top-of-rack switches configured according to a top-of-rack
architecture. In other embodiments, the leaf switches 212 can be aggregation switches
in any particular topology, such as end-of-row or middle-of-row topologies. In some
embodiments, the leaf switches 212 can also be implemented using aggregation switches.
[0061] Moreover, the topology illustrated in FIG. 2 and described herein is readily scalable
and may accommodate a large number of components, as well as more complicated arrangements
and configurations. For example, the network may include any number of fabrics 202,
which may be geographically dispersed or located in the same geographic area. Thus,
network nodes may be used in any suitable network topology, which may include any
number of servers, virtual machines or containers, switches, routers, appliances,
controllers, gateways, or other nodes interconnected to form a large and complex network.
Nodes may be coupled to other nodes or networks through one or more interfaces employing
any suitable wired or wireless connection, which provides a viable pathway for electronic
communications.
[0062] Network communications in the network fabric 202 can flow through the leaf switches
212. In some embodiments, the leaf switches 212 can provide endpoints (e.g., the servers
208), internal networks (e.g., the L2 network 204), or external networks (e.g., the
L3 network 206) access to the network fabric 202, and can connect the leaf switches
212 to each other. In some embodiments, the leaf switches 212 can connect endpoint
groups (EPGs) to the network fabric 202, internal networks (e.g., the L2 network 204),
and/or any external networks (e.g., the L3 network 206). EPGs are groupings of applications,
or application components, and tiers for implementing forwarding and policy logic.
EPGs can allow for separation of network policy, security, and forwarding from addressing
by using logical application boundaries. EPGs can be used in the network environment
200 for mapping applications in the network. For example, EPGs can comprise a grouping
of endpoints in the network indicating connectivity and policy for applications.
[0063] As discussed, the servers 208 can connect to the network fabric 202 via the leaf
switches 212. For example, the servers 208a and 208b can connect directly to the leaf
switches 212a and 212b, which can connect the servers 208a and 208b to the network
fabric 202 and/or any of the other leaf switches. The servers 208c and 208d can connect
to the leaf switches 212b and 212c via the L2 network 204. The servers 208c and 208d
and the L2 network 204 make up a local area network (LAN). LANs can connect nodes
over dedicated private communications links located in the same general physical location,
such as a building or campus.
[0064] The WAN 206 can connect to the leaf switches 212d or 212e via the L3 network 206.
WANs can connect geographically dispersed nodes over long-distance communications
links, such as common carrier telephone lines, optical light paths, synchronous optical
networks (SONET), or synchronous digital hierarchy (SDH) links. LANs and WANs can
include L2 and/or L3 networks and endpoints.
[0065] The Internet is an example of a WAN that connects disparate networks throughout the
world, providing global communication between nodes on various networks. The nodes
typically communicate over the network by exchanging discrete frames or packets of
data according to predefined protocols, such as the Transmission Control Protocol/Internet
Protocol (TCP/IP). In this context, a protocol can refer to a set of rules defining
how the nodes interact with each other. Computer networks may be further interconnected
by an intermediate network node, such as a router, to extend the effective size of
each network. The endpoints 208 can include any communication device or component,
such as a computer, server, blade, hypervisor, virtual machine, container, process
(e.g., running on a virtual machine), switch, router, gateway, host, device, external
network, etc.
[0066] In some embodiments, the network environment 200 also includes a network controller
running on the host 208a. The network controller is implemented using the Application
Policy Infrastructure Controller (APIC
™) from Cisco
®. The APIC
™ provides a centralized point of automation and management, policy programming, application
deployment, and health monitoring for the fabric 202. In some embodiments, the APIC
™ is operated as a replicated synchronized clustered controller. In other embodiments,
other configurations or software-defined networking (SDN) platforms can be utilized
for managing the fabric 202.
[0067] In some embodiments, a physical server 208 may have instantiated thereon a hypervisor
216 for creating and running one or more virtual switches (not shown) and one or more
virtual machines 218, as shown for the host 208b. In other embodiments, physical servers
may run a shared kernel for hosting containers. In yet other embodiments, the physical
server 208 can run other software for supporting other virtual partitioning approaches.
Networks in accordance with various embodiments may include any number of physical
servers hosting any number of virtual machines, containers, or other virtual partitions.
Hosts may also comprise blade/physical servers without virtual machines, containers,
or other virtual partitions, such as the servers 208a, 208c, 208d, and 208e.
[0068] The network environment 200 can also integrate a network traffic monitoring system,
such as the network traffic monitoring system 100 shown in FIG. 1. For example, the
network traffic monitoring system of FIG. 2 includes sensors 220a, 220b, 220c, and
220d (collectively, "220"), collectors 222, and an analytics engine, such as the analytics
engine 110 of FIG. 1, executing on the server 208e. The analytics engine 208e can
receive and process network traffic data collected by the collectors 222 and detected
by the sensors 220 placed on nodes located throughout the network environment 200.
Although the analytics engine 208e is shown to be a standalone network appliance in
FIG. 2, it will be appreciated that the analytics engine 208e can also be implemented
as a virtual partition (e.g., VM or container) that can be distributed onto a host
or cluster of hosts, software as a service (SaaS), or other suitable method of distribution.
In some embodiments, the sensors 220 run on the leaf switches 212 (e.g., the sensor
220a), the hosts 208 (e.g., the sensor 220b), the hypervisor 216 (e.g., the sensor
220c), and the VMs 218 (e.g., the sensor 220d). In other embodiments, the sensors
220 can also run on the spine switches 210, virtual switches, service appliances (e.g.,
firewall, deep packet inspector, traffic monitor, load balancer, etc.) and in between
network elements. In some embodiments, sensors 220 can be located at each (or nearly
every) network component to capture granular packet statistics and data at each hop
of data transmission. In other embodiments, the sensors 220 may not be installed in
all components or portions of the network (e.g., shared hosting environment in which
customers have exclusive control of some virtual machines).
[0069] As shown in FIG. 2, a host may include multiple sensors 220 running on the host (e.g.,
the host sensor 220b) and various components of the host (e.g., the hypervisor sensor
220c and the VM sensor 220d) so that all (or substantially all) packets traversing
the network environment 200 may be monitored. For example, if one of the VMs 218 running
on the host 208b receives a first packet from the WAN 206, the first packet may pass
through the border leaf switch 212d, the spine switch 210b, the leaf switch 212b,
the host 208b, the hypervisor 216, and the VM. Since all or nearly all of these components
contain a respective sensor, the first packet will likely be identified and reported
to one of the collectors 222. As another example, if a second packet is transmitted
from one of the VMs 218 running on the host 208b to the host 208d, sensors installed
along the data path, such as at the VM 218, the hypervisor 216, the host 208b, the
leaf switch 212b, and the host 208d will likely result in capture of metadata from
the second packet.
[0070] FIG. 3 illustrates an example of a data pipeline for generating network insights
based on collected network information, according to one aspect of the present disclosure.
The insights generated may include, for example, discovered applications or inventories,
application dependencies, policies, efficiencies, resource and bandwidth usage, network
flows and status of devices and/or associated users having access to the network can
be determined for the network using the network traffic data. In some embodiments,
the data pipeline 300 can be directed by a network traffic monitoring system, such
as the network traffic monitoring system 100 of FIG 1; an analytics engine, such as
the analytics engine 110 of FIG. 1; or other network service or network appliance.
For example, an analytics engine 110 can be configured to discover applications running
in the network, map the applications' interdependencies, generate a set of proposed
network policies for implementation, and monitor policy conformance and non-conformance
among other network-related tasks.
[0071] The data pipeline 300 includes a data collection stage 302 in which network traffic
data and corresponding data (e.g., host data, process data, user data, etc.) are captured
by sensors (e.g., the sensors 104 of FIG. 1) located throughout the network. The data
may comprise, for example, raw flow data and raw process data. As discussed, the data
can be captured from multiple perspectives to provide a comprehensive view of the
network. The data collected may also include other types of information, such as tenant
information, virtual partition information, out-of-band information, third party information,
and other relevant information. In some embodiments, the flow data and associated
data can be aggregated and summarized daily or according to another suitable increment
of time, and flow vectors, process vectors, host vectors, and other feature vectors
can be calculated during the data collection stage 302. This can substantially reduce
processing.
[0072] The data pipeline 300 may also include an input data stage 304 in which a network
or security administrator or other authorized user may configure insight generation
by selecting the date range of the flow data and associated data to analyze, and those
nodes for which the administrator wants to analyze. In some embodiments, the administrator
can also input side information, such as server load balance, route tags, and previously
identified clusters during the input data stage 304. In other embodiments, the side
information can be automatically pulled or another network element can push the side
information.
[0073] The next stage of the data pipeline 300 is pre-processing 306. During the pre-processing
stage 306, nodes of the network are partitioned into selected node and dependency
node subnets. Selected nodes are those nodes for which the user requests application
dependency maps and cluster information. Dependency nodes are those nodes that are
not explicitly selected by the users for an ADM run but are nodes that communicate
with the selected nodes. To obtain the partitioning information, edges of an application
dependency map (i.e., flow data) and unprocessed feature vectors can be analyzed.
[0074] Other tasks can also be performed during the pre-processing stage 306, including
identifying dependencies of the selected nodes and the dependency nodes; replacing
the dependency nodes with tags based on the dependency nodes' subnet names; extracting
feature vectors for the selected nodes, such as by aggregating daily vectors across
multiple days, calculating term frequency-inverse document frequency (tf-idf), and
normalizing the vectors (e.g., ℓ
2 normalization); and identifying existing clusters.
[0075] In some embodiments, the pre-processing stage 306 can include early feature fusion
pre-processing. Early fusion is a fusion scheme in which features are combined into
a single representation. Features may be derived from various domains (e.g., network,
host, virtual partition, process, user, etc.), and a feature vector in an early fusion
system may represent the concatenation of disparate feature types or domains.
[0076] Early fusion may be effective for features that are similar or have a similar structure
(e.g., fields of TCP and UDP packets or flows). Such features may be characterized
as being a same type or being within a same domain. Early fusion may be less effective
for distant features or features of different types or domains (e.g., flow-based features
versus process-based features). Thus, in some embodiments, only features in the network
domain (i.e., network traffic-based features, such as packet header information, number
of packets for a flow, number of bytes for a flow, and similar data) may be analyzed.
In other embodiments, analysis may be limited to features in the process domain (i.e.,
process-based features, such as process name, parent process, process owner, etc.).
In yet other embodiments, feature sets in other domains (e.g., the host domain, virtual
partition domain, user domain, etc.) may be the.
[0077] After pre-processing, the data pipeline 300 may proceed to an insight generation
stage 308. During the insight generation stage 308, the data collected and inputted
into the data pipeline 300 may be used to generate various network insights. For example,
an analytics engine 110 can be configured to discover of applications running in the
network, map the applications' interdependencies, generate a set of proposed network
policies for implementation, and monitor policy conformance and non-conformance among
other network-related tasks. Various machine learning techniques can be implemented
to analyze feature vectors within a single domain or across different domains to generate
insights. Machine learning is an area of computer science in which the goal is to
develop models using example observations (i.e., training data), that can be used
to make predictions on new observations. The models or logic are not based on theory
but are empirically based or data-driven.
[0078] After clusters are identified, the data pipeline 300 can include a post-processing
stage 310. The post-processing stage 310 can include tasks such as filtering insight
data, converting the insight data into a consumable format, or any other preparations
needed to prepare the insight data for consumption by an end user. At the output stage
312, the generated insights may be provided to an end user. The end user may be, for
example a network administrator, a third-party computing system, a computing system
in the network, or any other entity configured to receive the insight data. In some
cases, the insight data may be configured to be displayed on a screen or provided
to a system for further processing, consumption, or storage.
[0079] As noted above, a network traffic monitoring system may be configured to continually
collect network data and generate various insights based on the collected network
data. This network data and the insights may be updated over time and each set of
network data and/or insights may provide a network snapshot or view of the state of
the network for a particular period of time. The network snapshot may be generated
periodically over time or in response to one or more events. Events may include, for
example, a change to a network policy or configuration; an application experiencing
latency that exceeds an application latency threshold; the network experiencing latency
that exceeds a network latency threshold; failure of server, network device, or other
network element; and similar circumstances. Various network snapshots may further
be compared in order to identify changes in the state of the network over time and
be used to provide additional insights into the operations of the network.
[0080] With examples of network traffic monitoring systems, their operations, and network
environments in which they can be deployed described above, the disclosure now turns
to FIGs. 4 and 5, which discuss assessing application protectability and generating
a scheme for application protectability using the example network traffic monitoring
system of FIG. 1 within the example network environment 200 of FIG. 2.
[0081] Due to the network monitoring scheme described with reference to FIGs. 1, 2, and
3, an enterprise network environment monitor can gain detailed insight into what is
happening on the enterprise network environment. Data regarding device capabilities,
device behavior, user identities, and other relevant information can be gathered (e.g.,
as part of data collection process 302 of FIG. 3) in order to identify security layers,
assess the protectability of these security layers for an application, and generate
protectability schemes to improve application security. Such insights can be used
by a network controller such as network controller 118 of FIG. 1 to determine an objective
assessment of protectability of an application at any one of multiple network layers
to generate an application protectability index and subsequently generate a protectability
scheme (or replace/modify existing protectability scheme) for the corresponding application,
as will be further described below.
[0082] FIGs. 4A and 4B illustrate examples of an application security assessment system,
according to one aspect of the present disclosure. In FIG. 4A, a network controller
400 identifies network layers 420, 421, 422, and 423 which implement a protectability
scheme for application 410. Network controller 400 may be the same as network controller
118 described above with reference to FIG. 1.
[0083] Network controller 400 may perform a variety of functions including orchestrating
functions of an enterprise network. Such orchestration may be based on user provided
inputs/instructions/commands and/or may be automatic based on monitoring of network
conditions. Based on such inputs, network controller 400 can configure operations
of the network. Non-limiting examples of functionalities of network controller 400
include maintaining an inventory of devices and network components in the network,
along with their statuses; automating configuration and image updates for devices
and network components; analyzing network operations, including identifying potential
issues (performance, security, etc.) and suggesting solutions; and providing an integration
platform for other services, such as reporting systems.
[0084] As another example of such non-limiting functionalities, network controller 400 can
identify, for any application such as application 410, information associated with
its operation/execution. For example, network controller 400 can identify network
layers 420, 421, 422, and 423 associated with operation and execution of application
410. Non-limiting examples of network layers 420, 421, 422, and 423 include Kubernetes
(K-8), a container, Raspberry pi (RASP), an end point agent such as any of sensors
104 of FIG. 1, a firewall, a Server Load Balancer (SLB), etc. Traffic attempting to
access application 410 must pass through network layers 420, 421, 422, and 423. Each
network layer 420, 421, 422, and 423 can protect application 410 in its own way by
implementing correspondingly assigned network/traffic policies and controls. While
FIG. 4A illustrates four non-limiting example layers, the number of layers associated
with a particular application or workload is not limited to that shown in FIG. 4A
and may be more or less.
[0085] In FIG. 4B, network controller 400 can receive data from application 410, network
layers 420, 421, 422, 423, and the tools available at each of the network layers.
Network controller 400 can process these data to yield an application protectability
scheme for application 410.
[0086] Each of the layers 420, 421, 422 and 423 may have one or more corresponding tools.
For example, layer 420 can have tools 420-1, 420-2, 420-3, 420-4. Layer 421 can have
tools 421-1, 421-2, 421-3, 421-4. Layer 422 can have tools 422-1, 422-2, 422-3, 422-4,
and layer 423 can have tools 423-1, 423-2, 423-3, and 423-(which may be collectively
referred to as tools). While each layer in FIG. 4B is shown to have a total of four
tools, the number of tools is not limited thereto and may be more or less.
[0087] Tools shown in FIG. 4B can be any security tool, such as firewalls, load balancers,
workload protection platforms (and application segmentation), endpoint visibility
platforms, container orchestration platforms, etc. Within each network layer, the
tools can work together to achieve a specific goal for that network layer. For instance,
network layer 420 may have tools 420-1, 420-2, 420-3, and 420-4 designed to authenticate
and authorize traffic coming from outside the enterprise networking environment, while
another network layer 422 may have tools 422-1, 422-2, 422-3, and 422-4 designed to
protect application 410 from high traffic loads.
[0088] Once network controller 400 has data from network layers 420, 421, 422, and 423 and
their tools associated with application 410, it can determine a security index for
each network layer 420, 421, 422, and 423. A security index for a network layer is
an objective assessment of protectability of application 410 that the network layer
provides.
[0089] In some embodiments, a security index for network layer 420, 421, 422, or 423 is
defined as:
![](https://data.epo.org/publication-server/image?imagePath=2024/14/DOC/EPNWB1/EP21737899NWB1/imgb0001)
Where variables of formula (1) are defined as shown below:
Sl = the security index of layer l
Wl = the weight of layer l
kl = the number of tools in layer l
Wli = the weight of tool i in layer l
Fli = the security index factor of tool i in layer l
[0090] Weights for network layers 420, 421, 422 and 423 and corresponding tools shown in
FIG. 4B indicate the importance of a layer or tool for the protectability of application
410. If the enterprise networking environment hosts multiple applications 410, these
weights can change for each application 410, even if the network layer and tools are
the same. Such weights can depend on factors including, but not limited to, importance/criticality
of a corresponding application, etc. Weights can be configurable parameters determined
based on experiments and/or empirical studies.
[0091] The value of a security index factor
Fli in formula (1) for any given tool
i (a tool
i may be any one of example tools shown in FIG. 4B) in a network layer / (a layer
l may be any one of example layers 420, 421, 422 and 423 shown in FIG. 4A) depends
on how tool
i protects application 410. It is specific to a given tool
i and how it relates to application 410. In some embodiments, example security index
factors for different kinds of tools can be determined as shown below:
[0092] Once network controller 400 computes a security index for each network layer 420,
421, 422, and 423 per formula (1), network controller 400 can compute an application
protectability index for application 410 per formula (7) below. The application protectability
index is a composite measure of how application 410 is protected at different network
layers 420, 421, 422, and 423. In some embodiments, an application protectability
index can be defined as
![](https://data.epo.org/publication-server/image?imagePath=2024/14/DOC/EPNWB1/EP21737899NWB1/imgb0007)
Where variables of formula (7) are defined as shown below:,
m = the number of layers l protecting application 410
Sl = the security index of layer l
Wl = the weight of layer l
[0093] Once an application protectability index for the given application protectability
scheme provided by network layers 420, 421, 422, and 423 is generated, network controller
400 can generate suggestions for creating and/or replacing/improving application protectability
schemes for a given application such as application 410. Using information gathered
from network layers 420, 421, 422, and 423 and corresponding example tools shown in
FIG. 4B, network controller 400 can generate application protectability indices for
alternative application protectability schemes. By comparing the existing application
protectability index to potential alternatives, network controller 400 can determine
which application protectability scheme would optimally protect application 410. For
example, application protection using Kubernetes may lead to a higher application
protectability index relative to application protection using a default networking
environment firewall. If application 410 is currently protected at the firewall, then
providing a recommendation to remove protectability at the firewall and replace it
with protectability at KUBERNETES would be an improvement. Once an optimal or improved
application protectability scheme has been determined, network controller 400 can
provide this application protectability scheme to a network administrator for approval
and/or other network components such as network controller 118 for implementation.
[0094] FIG. 5 illustrates an example method for determining an application protectability
index, according to one aspect of the present disclosure. FIG. 5 describes a process
by which network controller 400 determines an objective assessment of protectability
of an application at any one of multiple network layers to generate an application
protectability index and subsequently generate a protectability scheme. The process
of FIG. 5 will be described from the perspective of network controller 400 described
above with reference to FIGs. 4A and 4B. However, it will be understood that network
controller 400 may be implemented via one or more of the components described in FIG.
1 such as network controller 118. Furthermore, FIG. 5 will be described with reference
to components of FIGs. 4A and 4B.
[0095] The method begins at operation 500 when network controller 400 identifies network
layers 420 associated with application 410. These network layers 420 can contain tools
430 which are used to protect application 410. At operation 510, network controller
400 receives data from network layers 420, tools 430, and application 410. This data
can include the functions and capabilities of network layers 420 and tools 430, as
well as data about traffic passing through network layers 420 and handled by application
410.
[0096] Once data is received, at operation 520 network controller 400 determines a security
index for application 410 for each network layer 420. A security index is an objective
assessment of protectability of application 410 at a network layer 420.
[0097] At operation 530, once a security index has been determined for each network layer
420, network controller 400 determines an application protectability index for application
410. The application protectability index accounts for each network layer 420 associated
with application 410, and offers an overall measure of the current protectability
scheme for application 410. Network controller 400 may determine the security index
of each layer per the process described above with reference to FIG. 4 and formulas
(1) through (7).
[0098] At operation 540, network controller 400 generates (creates) an application protectability
scheme based on the determined application protectability index determined per operation
530. For example, if the application protectability index is above a given threshold
network controller can determine that the existing application protectability scheme
is sufficient. If the application protectability index is low, network controller
400 can determine application protectability indices for alternate application protectability
schemes. It can provide such schemes to a network administrator or service for consumption.
[0099] In another example, network controller 400 may perform the process of FIG. 5 continuously
and if at any given iteration, a protectability index for a particular protectability
scheme is determined that is higher than a current protectability index in place for
a given application, then at operation 540, the current protectability index may be
replaced with the protectability index determined at that specific iteration.
[0100] In some embodiments, network controller 400 can implement the application protectability
scheme generated at operation 540. This can involve choosing new network layers or
tools to protect application 410, or removing network layers or tools which are currently
protecting application 410. In some embodiments, network controller 400 may send the
application protectability scheme to a network administrator for approval, as shown
in FIG. 4B. In some embodiments, this can involve comparing the current scheme with
other possible application protectability schemes, where the possible schemes are
stored in a metadata/document store.
[0101] Network controller 400 can generate possible application protectability schemes with
layers, and can create multiple possible application protectability schemes. Implementing
a new application protectability scheme can involve adding new layers, subtracting
existing layers, or modifying the implementation of existing layers. In some embodiments,
implementation of the application protectability scheme can involve proposing changes
to a network administrator, client, or other decision-maker, and implementing the
changes after receiving approval.
[0102] As one example, an API for a given application (as determined per operation 530),
which is already running a firewall and a container orchestration platform, may result
in network controller 400 recommending additional protectability scheme(s) such as
using an endpoint visibility platform such as CISCO Advanced Malware Protection, developed
by Cisco, Inc. of San Jose, CA and a workload protection platform such as CICSO Tetration,
developed by Cisco, Inc. of San Jose, CA.
[0103] With systems and processes for determining an objective assessment of protectability
of an application at any one of multiple network layers to generate an application
protectability index, and subsequently generating an application protectability scheme
based on that index, described with reference to FIGs. 4A-B and 5, the disclosure
now turns to FIG. 6, which illustrates an example computing device, according to one
aspect of the present disclosure.
[0104] FIG. 6 shows an example of computing system 600, which can be for example any computing
device making up network controller 400 or any component thereof in which the components
of the system are in communication with each other using connection 605. Connection
605 can be a physical connection via a bus, or a direct connection into processor
610, such as in a chipset architecture. Connection 605 can also be a virtual connection,
networked connection, or logical connection.
[0105] In some embodiments computing system 600 is a distributed system in which the functions
described in this disclosure can be distributed within a datacenter, multiple datacenters,
a peer network, etc. In some embodiments, one or more of the described system components
represents many such components each performing some or all of the function for which
the component is described. In some embodiments, the components can be physical or
virtual devices.
[0106] Example computing system 600 includes at least one processing unit (CPU or processor)
610 and connection 605 that couples various system components including system memory
615, such as read only memory (ROM) 620 and random access memory (RAM) 625 to processor
610. Computing system 600 can include a cache of high-speed memory 612 connected directly
with, in close proximity to, or integrated as part of processor 610.
[0107] Processor 610 can include any general purpose processor and a hardware service or
software service, such as services 632, 634, and 636 stored in storage device 630,
configured to control processor 610 as well as a special-purpose processor where software
instructions are incorporated into the actual processor design. Processor 610 may
essentially be a completely self-contained computing system, containing multiple cores
or processors, a bus, memory controller, cache, etc. A multi-core processor may be
symmetric or asymmetric.
[0108] To enable user interaction, computing system 600 includes an input device 645, which
can represent any number of input mechanisms, such as a microphone for speech, a touch-sensitive
screen for gesture or graphical input, keyboard, mouse, motion input, speech, etc.
Computing system 600 can also include output device 635, which can be one or more
of a number of output mechanisms known to those of skill in the art. In some instances,
multimodal systems can enable a user to provide multiple types of input/output to
communicate with computing system 600. Computing system 600 can include communications
interface 640, which can generally govern and manage the user input and system output.
There is no restriction on operating on any particular hardware arrangement and therefore
the basic features here may easily be substituted for improved hardware or firmware
arrangements as they are developed.
[0109] Storage device 630 can be a non-volatile memory device and can be a hard disk or
other types of computer readable media which can store data that are accessible by
a computer, such as magnetic cassettes, flash memory cards, solid state memory devices,
digital versatile disks, cartridges, random access memories (RAMs), read only memory
(ROM), and/or some combination of these devices.
[0110] The storage device 630 can include software services, servers, services, etc., that
when the code that defines such software is executed by the processor 610, it causes
the system to perform a function. In some embodiments, a hardware service that performs
a particular function can include the software component stored in a computer-readable
medium in connection with the necessary hardware components, such as processor 610,
connection 605, output device 635, etc., to carry out the function.
[0111] For clarity of explanation, in some instances the various embodiments may be presented
as including individual functional blocks including functional blocks comprising devices,
device components, steps or routines in a method embodied in software, or combinations
of hardware and software.
[0112] In some embodiments the computer-readable storage devices, mediums, and memories
can include a cable or wireless signal containing a bit stream and the like. However,
when mentioned, non-transitory computer-readable storage media expressly exclude media
such as energy, carrier signals, electromagnetic waves, and signals per se.
[0113] Methods according to the above-described examples can be implemented using computer-executable
instructions that are stored or otherwise available from computer readable media.
Such instructions can comprise, for example, instructions and data which cause or
otherwise configure a general purpose computer, special purpose computer, or special
purpose processing device to perform a certain function or group of functions. Portions
of computer resources used can be accessible over a network. The computer executable
instructions may be, for example, binaries, intermediate format instructions such
as assembly language, firmware, or source code. Examples of computer-readable media
that may be used to store instructions, information used, and/or information created
during methods according to described examples include magnetic or optical disks,
flash memory, USB devices provided with non-volatile memory, networked storage devices,
and so on.
[0114] Devices implementing methods according to these disclosures can comprise hardware,
firmware, and/or software, and can take any of a variety of form factors. Typical
examples of such form factors include laptops, smart phones, small form factor personal
computers, personal digital assistants, rackmount devices, standalone devices, and
so on. Functionality described herein also can be embodied in peripherals or add-in
cards. Such functionality can also be implemented on a circuit board among different
chips or different processes executing in a single device, by way of further example.
[0115] The instructions, media for conveying such instructions, computing resources for
executing them, and other structures for supporting such computing resources are means
for providing the functions described in these disclosures.
[0116] Although a variety of examples and other information was used to explain aspects
within the scope of the appended claims, no limitation of the claims should be implied
based on particular features or arrangements in such examples, as one of ordinary
skill would be able to use these examples to derive a wide variety of implementations.
Further and although some subject matter may have been described in language specific
to examples of structural features and/or method steps, it is to be understood that
the subject matter defined in the appended claims is not necessarily limited to these
described features or acts. For example, such functionality can be distributed differently
or performed in components other than those identified herein. Rather, the described
features and steps are disclosed as examples of components of systems and methods
within the scope of the appended claims.